darknet2inferx

This model convertor ported from original supports conversion from darkent to caffe, especially for YOLOv2 and tiny-YOLO etc.

Step1 Caffe Environment

First, ensure caffe installed (converison progress'll use Python interface of caffe), recommanding using Docker image of bvlc/caffe:cpu instead.

Step2 Convert

Use following command, convert darknet model to caffe's:

python darknet2caffe.py DARKNET_CFG DARKNET_WEIGHTS

If last message shows as below, it means successful conversion from darknet to caffe:

Network initialization done.

Next is conversion from caffe to InferXLite:

python caffe2inferx.py CAFFE_PROTOTXT CAFFE_CAFFEMODEL

Appendix

darknet2inferx

Translate to InferXLite directly from darknet:

python darknet2inferx.py DARKNET_CFG DARKNET_WEIGHTS

Check exectuion log in darknet2caffe_convert.log.

Translate *.cfg file to *.prototxt only:

python cfg.py DARKNET_CFG

Old API

Due to the newest API starting with inferx_ in *.c file (such as inferx_convolution), if use old API (without inferx_), you should convert to old API using command below:

python to_old_api_for_c_file.py INFERX_MODEL_C 

TODO

• [x] auto shape infer for output dimension of reorg layer from darknet to caffe, especially for one-reorg-layer networks like YOLOv2.
• [x] darknet2inferx
  • [x] support converison of region layer's parameters to variables in *.h file.
  • [x] support yolo_pooling judge/choose in pooling conversion from caffe to inferxlite [DELAY]
• [x] darknet2caffe
  • [x] support conversion of pooling layer for a special case (input shape same as output shape. More concretely, stride=1 size=2 max pooling, this case's process of darknet will pad 1 for right and down side of input feature map. Thus, this conversion replaces stride=1 size=2 with stride=1 size=1 before cfg2proto. After conversion from weights to caffemodel, an afterward process'll replace pooling setting in cfg file using ground truth params (stride and size) in cfg file).